Metadata-Version: 2.4
Name: icx
Version: 1.0.1
Summary: This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems. 
Project-URL: Homepage, https://github.com/maddiewaller/Individual-Consistency-eXplorer/
Author-email: Madeleine Waller <madeleine.waller@kcl.ac.uk>
License-Expression: MIT
License-File: LICENSE.md
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.0
Requires-Dist: gower
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: streamlit
Requires-Dist: streamlit-aggrid
Requires-Dist: streamlit-sortables
Description-Content-Type: text/markdown

# Individual Consistency eXplorer (icx) Python Package

This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems.

The dashboard allows users to:
* Explore and operate on a tabular dataset of individuals provided with their corresponding binary classifications;
* Define how similarity between individuals is measured, by configuring categorisation of attributes and how distances between attribute values are computed;
* Compute and visualise five individual fairness metrics that summarise the consistency of classifications across the dataset; and
* Inspect attributes of specific individuals and of those individuals most similar to them, to explore variations in attribute values and allow like-for-like comparisons of classifications.

This package implements the functionality described in a ECAI Demo Track paper 2025, available at [Individual Consistency eXplorer (ICX): An Interactive Dashboard for the Exploration of Individual Fairness](https://kclpure.kcl.ac.uk/ws/portalfiles/portal/345016883/ECAI-25-Demos_paper_71.pdf)

To see an online version of the dashboard, see [Individual Consistency eXplorer Online](https://individual-consistency-explorer.streamlit.app).

To see an online version of the dashboard, see [Individual Consistency eXplorer Online](https://individual-consistency-explorer.streamlit.app).


## 📦 Installation

It is recommended to install icx in a virtual environment (e.g., conda).

```
pip install icx
```

## Basic Usage

```
from icx import dashboard
```

### Run the dashboard

```
dashboard.run()
```

In the dashboard there is the ability to upload your own datasets or use the demo datasets provided.


Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
